{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "93da69f1-1289-4f17-86dd-b34fca2be8b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T03:35:49.668176Z",
     "iopub.status.busy": "2022-05-30T03:35:49.667307Z",
     "iopub.status.idle": "2022-05-30T03:35:53.827414Z",
     "shell.execute_reply": "2022-05-30T03:35:53.826712Z",
     "shell.execute_reply.started": "2022-05-30T03:35:49.668058Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.22 s, sys: 107 ms, total: 2.32 s\n",
      "Wall time: 4.15 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7cbd2968-159e-48c1-91d3-8ac2d6cd690d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T03:36:52.784411Z",
     "iopub.status.busy": "2022-05-30T03:36:52.783873Z",
     "iopub.status.idle": "2022-05-30T03:36:52.789308Z",
     "shell.execute_reply": "2022-05-30T03:36:52.788674Z",
     "shell.execute_reply.started": "2022-05-30T03:36:52.784376Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
    "print(f\"Using {device} device\")\n",
    "\n",
    "if torch.cuda.is_available():\n",
    "    torch.set_default_tensor_type(torch.cuda.FloatTensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8cadb0bb-f5b1-4c45-a6da-894a388d4ac5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T03:37:04.302279Z",
     "iopub.status.busy": "2022-05-30T03:37:04.301701Z",
     "iopub.status.idle": "2022-05-30T03:37:07.991781Z",
     "shell.execute_reply": "2022-05-30T03:37:07.991092Z",
     "shell.execute_reply.started": "2022-05-30T03:37:04.302245Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.29 s, sys: 492 ms, total: 3.78 s\n",
      "Wall time: 3.68 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train = pd.read_csv('../data/mnist/train.csv', header=None).values\n",
    "train, train_label = torch.tensor(train[...,1:]/255, dtype=torch.float), torch.tensor(train[...,0], dtype=torch.long)\n",
    "# 不需要one-hot\n",
    "# train_label_h = torch.tensor(pd.get_dummies(train_label).values, dtype=torch.float)\n",
    "\n",
    "test = pd.read_csv('../data/mnist/test.csv', header=None).values\n",
    "test, test_label = torch.tensor(test[...,1:]/255, dtype=torch.float), torch.tensor(test[...,0], dtype=torch.long)\n",
    "# test_label_h = torch.tensor(pd.get_dummies(test_label).values, dtype=torch.float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "33a4a5a0-9dc0-4358-84bb-464a80fa8a6a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T03:37:14.304916Z",
     "iopub.status.busy": "2022-05-30T03:37:14.303922Z",
     "iopub.status.idle": "2022-05-30T03:37:14.313750Z",
     "shell.execute_reply": "2022-05-30T03:37:14.312820Z",
     "shell.execute_reply.started": "2022-05-30T03:37:14.304872Z"
    }
   },
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    \n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(784, 200),\n",
    "            nn.LeakyReLU(0.02),\n",
    "            nn.LayerNorm(200), # 基础强化\n",
    "            nn.LeakyReLU(0.02),\n",
    "            nn.Linear(200, 1),\n",
    "            nn.Sigmoid())\n",
    "        \n",
    "        # self.loss = nn.MSELoss() # 不知道为什么就不行\n",
    "        self.loss = nn.BCELoss()\n",
    "        self.optimiser = torch.optim.Adam(self.parameters(), lr=0.0001)\n",
    "        self.counter, self.progress = 0, [] # 一个用来记录训练了多少次，一个用来记录每次的损失\n",
    "    \n",
    "    def train(self, data: 'nx784的tensor', label: 'nx1的tensor'):\n",
    "        res = self.model(data)\n",
    "        loss = self.loss(res, label)\n",
    "        \n",
    "        self.optimiser.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimiser.step()\n",
    "        \n",
    "        self.counter += 1\n",
    "        if not self.counter % 10:self.progress.append(loss.item())\n",
    "        if not self.counter % 1000:print(f\"训练了{self.counter}次\")\n",
    "    \n",
    "    def show_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=['loss'])\n",
    "        df.plot(ylim=(0), figsize=(16,8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5, 1.0, 5.0))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b3a42d1f-8f1b-43ad-a954-aa49477db6cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T03:37:32.429887Z",
     "iopub.status.busy": "2022-05-30T03:37:32.429067Z",
     "iopub.status.idle": "2022-05-30T03:37:42.291791Z",
     "shell.execute_reply": "2022-05-30T03:37:42.291211Z",
     "shell.execute_reply.started": "2022-05-30T03:37:32.429843Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练了1000次\n",
      "训练了2000次\n",
      "训练了3000次\n",
      "训练了4000次\n",
      "训练了5000次\n",
      "训练了6000次\n",
      "CPU times: user 37.3 s, sys: 121 ms, total: 37.4 s\n",
      "Wall time: 9.7 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "D = Discriminator()\n",
    "for i in range(3000):\n",
    "    D.train(train[0], torch.FloatTensor([1.0]))\n",
    "    D.train(torch.randn(784), torch.FloatTensor([0]))\n",
    "D.show_progress()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1bcf4e68-988a-4ff4-afad-99a583d4dd63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T03:37:49.424478Z",
     "iopub.status.busy": "2022-05-30T03:37:49.423806Z",
     "iopub.status.idle": "2022-05-30T03:37:49.432888Z",
     "shell.execute_reply": "2022-05-30T03:37:49.432062Z",
     "shell.execute_reply.started": "2022-05-30T03:37:49.424449Z"
    }
   },
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        \n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(100, 200),\n",
    "            nn.LeakyReLU(0.02),\n",
    "            nn.LayerNorm(200),\n",
    "            nn.Linear(200, 784),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "        \n",
    "        self.optimiser = torch.optim.Adam(self.parameters(), lr=0.0001)\n",
    "        self.counter, self.progress = 0, []\n",
    "    \n",
    "    \n",
    "    def train(self, D, data): # 有个data是为了保证种子可控\n",
    "        \n",
    "        g_output = self.model(data)\n",
    "        res = D.model(g_output)\n",
    "        loss = D.loss(res, torch.ones(len(data),1))\n",
    "        \n",
    "        self.optimiser.zero_grad()\n",
    "        loss.backward()\n",
    "        self.optimiser.step()\n",
    "        \n",
    "        self.counter += 1\n",
    "        if not self.counter % 10:self.progress.append(loss.item())\n",
    "        if not self.counter % 1000:print(f\"训练了{self.counter}次\")\n",
    "        \n",
    "        \n",
    "    def show_progress(self):\n",
    "        df = pd.DataFrame(self.progress, columns=['loss'])\n",
    "        df.plot(ylim=(0), figsize=(16,8), alpha=0.1, marker='.', grid=True, yticks=(0, 0.25, 0.5, 1.0, 5.0))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0039fd70-0ad9-4295-90c9-a5605606f808",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T03:37:55.317477Z",
     "iopub.status.busy": "2022-05-30T03:37:55.316888Z",
     "iopub.status.idle": "2022-05-30T03:37:55.508739Z",
     "shell.execute_reply": "2022-05-30T03:37:55.508015Z",
     "shell.execute_reply.started": "2022-05-30T03:37:55.317452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc74dfb2100>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "G = Generator()\n",
    "output = G.model(torch.randn(100))\n",
    "img = output.detach().numpy().reshape(28,28)\n",
    "plt.imshow(img, interpolation='none', cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f9912813-6f56-4e50-8c81-2f7db93dba50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-05-30T03:38:02.302045Z",
     "iopub.status.busy": "2022-05-30T03:38:02.301738Z",
     "iopub.status.idle": "2022-05-30T03:38:02.308035Z",
     "shell.execute_reply": "2022-05-30T03:38:02.307339Z",
     "shell.execute_reply.started": "2022-05-30T03:38:02.302015Z"
    }
   },
   "outputs": [],
   "source": [
    "def show(G):\n",
    "    f, axarr = plt.subplots(2,3, figsize=(16,8))\n",
    "    for i in range(2):\n",
    "        for j in range(3):\n",
    "            output = G.model(torch.randn(100))\n",
    "            img = output.detach().numpy().reshape(28,28)\n",
    "            axarr[i,j].imshow(img, interpolation='none', cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ce4f08cd-bdc0-4acf-95fa-a6d03370e84c",
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2022-05-30T03:38:07.834172Z",
     "iopub.status.busy": "2022-05-30T03:38:07.833475Z",
     "iopub.status.idle": "2022-05-30T03:44:27.597990Z",
     "shell.execute_reply": "2022-05-30T03:44:27.597388Z",
     "shell.execute_reply.started": "2022-05-30T03:38:07.834128Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch =  1\n",
      "训练了1000次\n",
      "训练了2000次\n",
      "训练了1000次\n",
      "训练了3000次\n",
      "训练了4000次\n",
      "训练了2000次\n",
      "训练了5000次\n",
      "训练了6000次\n",
      "训练了3000次\n",
      "训练了7000次\n",
      "训练了8000次\n",
      "训练了4000次\n",
      "训练了9000次\n",
      "训练了10000次\n",
      "训练了5000次\n",
      "epoch =  2\n",
      "训练了11000次\n",
      "训练了12000次\n",
      "训练了6000次\n",
      "训练了13000次\n",
      "训练了14000次\n",
      "训练了7000次\n",
      "训练了15000次\n",
      "训练了16000次\n",
      "训练了8000次\n",
      "训练了17000次\n",
      "训练了18000次\n",
      "训练了9000次\n",
      "训练了19000次\n",
      "训练了20000次\n",
      "训练了10000次\n",
      "epoch =  3\n",
      "训练了21000次\n",
      "训练了22000次\n",
      "训练了11000次\n",
      "训练了23000次\n",
      "训练了24000次\n",
      "训练了12000次\n",
      "训练了25000次\n",
      "训练了26000次\n",
      "训练了13000次\n",
      "训练了27000次\n",
      "训练了28000次\n",
      "训练了14000次\n",
      "训练了29000次\n",
      "训练了30000次\n",
      "训练了15000次\n",
      "epoch =  4\n",
      "训练了31000次\n",
      "训练了32000次\n",
      "训练了16000次\n",
      "训练了33000次\n",
      "训练了34000次\n",
      "训练了17000次\n",
      "训练了35000次\n",
      "训练了36000次\n",
      "训练了18000次\n",
      "训练了37000次\n",
      "训练了38000次\n",
      "训练了19000次\n",
      "训练了39000次\n",
      "训练了40000次\n",
      "训练了20000次\n",
      "epoch =  5\n",
      "训练了41000次\n",
      "训练了42000次\n",
      "训练了21000次\n",
      "训练了43000次\n",
      "训练了44000次\n",
      "训练了22000次\n",
      "训练了45000次\n",
      "训练了46000次\n",
      "训练了23000次\n",
      "训练了47000次\n",
      "训练了48000次\n",
      "训练了24000次\n",
      "训练了49000次\n",
      "训练了50000次\n",
      "训练了25000次\n",
      "epoch =  6\n",
      "训练了51000次\n",
      "训练了52000次\n",
      "训练了26000次\n",
      "训练了53000次\n",
      "训练了54000次\n",
      "训练了27000次\n",
      "训练了55000次\n",
      "训练了56000次\n",
      "训练了28000次\n",
      "训练了57000次\n",
      "训练了58000次\n",
      "训练了29000次\n",
      "训练了59000次\n",
      "训练了60000次\n",
      "训练了30000次\n",
      "epoch =  7\n",
      "训练了61000次\n",
      "训练了62000次\n",
      "训练了31000次\n",
      "训练了63000次\n",
      "训练了64000次\n",
      "训练了32000次\n",
      "训练了65000次\n",
      "训练了66000次\n",
      "训练了33000次\n",
      "训练了67000次\n",
      "训练了68000次\n",
      "训练了34000次\n",
      "训练了69000次\n",
      "训练了70000次\n",
      "训练了35000次\n",
      "epoch =  8\n",
      "训练了71000次\n",
      "训练了72000次\n",
      "训练了36000次\n",
      "训练了73000次\n",
      "训练了74000次\n",
      "训练了37000次\n",
      "训练了75000次\n",
      "训练了76000次\n",
      "训练了38000次\n",
      "训练了77000次\n",
      "训练了78000次\n",
      "训练了39000次\n",
      "训练了79000次\n",
      "训练了80000次\n",
      "训练了40000次\n",
      "CPU times: user 24min 37s, sys: 4.17 s, total: 24min 41s\n",
      "Wall time: 6min 15s\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fyn88MAHUu0tErwPQGC4R/Q6ouX4HL3e/Q9KqGtQCAIWh1wFoFPQ7oBiD+RuvU83sQTO72MzC34MwszlmNs/M5nWu7Iw+DADKil4HoFH02+/odUD1qh28LpS0u6R9JC2TdF70ge4+191nufusjvaOKg8HAIWg1wFoFAPqd/Q6oHpVDV7uvtzde9y9V9IPJc0e2rIAoHj0OgCNgn4H5FfV4GVmU7d692hJDw9NOQBQHvQ6AI2CfgfkN5B18ldJOkhSu5ktkXS2pIPMbB9JLmmRpJPylYgyGdMWf8nMPOLIMFv48+vjG22Kb3PS2NaBlAUM2nDtdYtffCXMnly5LsxeO2V88nZ3mBCvKc9x6owyGZFaebw5XhNdtZdWJONxo/r9Vg78heHa73Lo7Y1Ph7O+qyfMxiWeL+06uT5OhzNyZHWnJUp5867p02MMd/12a3c/vo+LL8pQCwAUhl4HoFHQ74BiDGarIQAAAABgABi8AAAAACAzBi8AAAAAyIzBCwAAAAAyY/ACAAAAgMzYQYtt0t3TG2YLH3y6qtts3eNNYTZ+1NCvMgWGm9Tjsqs7zt575k/D7HOfOCR5zOPfMC3Mpk0albwuts2hx707mbe1NNWoEmD4cY/XxUtS6uwY2w/zU94svve+qq6347veE2bNTY39mk9jf/YAAAAAUAMMXgAAAACQGYMXAAAAAGTG4AUAAAAAmTF4AQAAAEBmDF4AAAAAkFnDrpNPrV9OGZHaKyppxIh0Xu/2+MT1cfjcQ3GWuN8euuCY6gsCkOxLmzYnel3nM2H0lfN+kTzmv0/fKcwe+/bRYdbWWh+rz3t74xXTX7/9yfiK/aymrsal/2u/Ib9NAAOTaAVqGgZP+Sa+8wtxuG5VnCW+79z/lcMGUdHwxiteAAAAAJAZgxcAAAAAZMbgBQAAAACZMXgBAAAAQGYMXgAAAACQGYMXAAAAAGQ2rNfJp1bGf+22eB3wCy9vCrMr56ZXLN/47x8JswNe0x5m1s+a+qHWk9iP2n7kd+IrvpBYo5zSOiqMJk9oq+42AUhKn8Zic+rUGd1dcbYiXjUvSWsS+Rdufn2YffXwPcOsuam6nwV6YoV7qtd98Ir7wuy/fnx7fMAXF8dZqpePHBNGB3/wvWHW1lIfK/iBepQ85YbSPXRsW/w0utbP61J9cNLbPxNfcdMrVR3vS+d/MsxamnldJ8I9AwAAAACZMXgBAAAAQGYMXgAAAACQGYMXAAAAAGTG4AUAAAAAmTF4AQAAAEBm/a6TN7Ppki6TtIOkXklz3f07ZjZJ0tWSZkhaJOlYd1+dr9Rtt25jd5gtXrUhzH5y3o/iG02s65Skv//A18Lsl5d/Psz222W7MKt2LWdyZfxxF8dXrHZlfGJ16gXfPbW62wRqpJ57XcqeU8fF4ajxcba+n0+xuTWMrr91YZj9Zv7zYfaLTxwQZqvWbw6zv/7o3DDTSy/EWVf8faBq7TuH0Q57x2v2rz1x9tDXAvRhuPa61HOezsRpgr7067hfSdIF7927qnpS692rXTWf+hx3/dhP4itWuTL+6xecHmYffeuuVd1moxvIM/puSae7++skvVXSKWa2l6QzJd3q7jMl3Vp5HwDqFb0OQCOg1wEF6Xfwcvdl7j6/8vZaSQskTZN0pKRLKx92qaSjMtUIANnR6wA0AnodUJxt+h02M5shaV9Jd0ua4u7LpC0PYkmTh7w6ACgAvQ5AI6DXAbU14MHLzMZK+qmkT7r7y9twvTlmNs/M5nWu7KymRgCoGXodgEZArwNqb0CDl5m1aMuD8wp3/1nl4uVmNrWST5W0oq/ruvtcd5/l7rM62juGomYAyIJeB6AR0OuAYvQ7eNmW1SsXSVrg7udvFd0o6YTK2ydIumHoywOA2qDXAWgE9DqgOP2uk5e0v6T3S3rIzO6vXHaWpHMlXWNmJ0p6TtIxWSochAmjW8Js3Kg4629lfFJ3VxgddtwX4uu1jQ2jpbd+JcweXLwmzD51zQPx8Z65L86qdMPl8ed34B78VAylV7e9LqWttSnMPvTJ48Ls/37zsvQNj5kYRp2/vzXORsT1vP6JeNV897ML4lo2rouzHLafHka//9HJYfa6aYn1/UDtDMtel/L+S+eF2byrrk1ed8Wa/y/Mzjl0zzB7/U5D/3hfvyk+TdLa+++q6jYPPvkDYcbK+KHX7+Dl7ndKik44cPDQlgMAxaDXAWgE9DqgONWdmRcAAAAAMGAMXgAAAACQGYMXAAAAAGTG4AUAAAAAmTF4AQAAAEBmDF4AAAAAkNlAzuNVt7acI7BvXzx0jzC7+NyR8Y1u3jSYkmKJ89DseODp8fW2mxpna5bHWeJcOinP3PaNuJQxrVXdJoBinH/kXmF28uzPJa/7ln/6ZnUHHT0hjLqfeTi+XteG6o5XrUSdN3z3w2HGubqAYnjiHKyrVyf6Rz/nbr117hVx9su9w2zH3aaF2YsvrAqz5pb4qfkOO20fZqnzwa649cth1tLMazC1xL0NAAAAAJkxeAEAAABAZgxeAAAAAJAZgxcAAAAAZMbgBQAAAACZMXgBAAAAQGbDep18ypiR8Tr1O685O8wOeH8/K5TXxStCq9bTHWerloTRdf/5hTB7+8z2MGsaEa/hB9AY9pg6LpkvvOGsMJt5xJfiK76yJs56e+KsdVScTZgSRnfOnRNmM9pHh9noxPeI1KlKABSjuSl+LeGSE2eH2d8sXpm83VcevCsOFz0QRksTWaqf7XjwIWH2h88fHGYt58TXQ3nwihcAAAAAZMbgBQAAAACZMXgBAAAAQGYMXgAAAACQGYMXAAAAAGTG4AUAAAAAmTXsOvnUOuDX7zQ+zFbd9sXk7T734oY4W70+zJat3xhmL2+K18m/b+9pYTZxTGuYAcBgtI8bGWarf/uVGlYCAGl7Jk6PcfWZ6TXsf3z+zWH2hiljw+xN0yeFWXPitD0TRrck60F94xUvAAAAAMiMwQsAAAAAMmPwAgAAAIDMGLwAAAAAIDMGLwAAAADIjMELAAAAADLrd528mU2XdJmkHST1Sprr7t8xs3MkfVRSZ+VDz3L3m3IVWhapNfSStEv76KoyAMWi1wFoBI3Y61qa49cZDpjZnrxufzmwLQZyHq9uSae7+3wzGyfpXjO7pZJ9y92/ma88AKgZeh2ARkCvAwrS7+Dl7sskLau8vdbMFkiKz9oLAHWIXgegEdDrgOJs0994mdkMSftKurty0alm9qCZXWxmE4PrzDGzeWY2r3NlZ18fAgClQq8D0AjodUBtDXjwMrOxkn4q6ZPu/rKkCyXtLmkfbfnJyXl9Xc/d57r7LHef1dHeMfiKASAjeh2ARkCvA2pvQIOXmbVoy4PzCnf/mSS5+3J373H3Xkk/lDQ7X5kAkB+9DkAjoNcBxeh38LIta/wukrTA3c/f6vKpW33Y0ZIeHvryAKA26HUAGgG9DijOQLYa7i/p/ZIeMrP7K5edJel4M9tHkktaJOmkDPUBQK3Q6wA0AnodUJCBbDW8U1JfJ68aFud2AACJXgegMdDrgOJs01ZDAAAAAMC2Y/ACAAAAgMwYvAAAAAAgMwYvAAAAAMiMwQsAAAAAMmPwAgAAAIDMGLwAAAAAIDMGLwAAAADIjMELAAAAADJj8AIAAACAzBi8AAAAACAzBi8AAAAAyMzcvXYHM+uU9Gzl3XZJK2t28P6VqR5qiZWpnqGqZRd37xiC20FJvKrXScPz63YoUEusTPXQ69Anet2AlakWqVz1DMdawl5X08HrLw5sNs/dZxVy8D6UqR5qiZWpnjLVgnIr09cKtfStTLVI5aqnTLWg3Mr0tUItsTLV02i18KuGAAAAAJAZgxcAAAAAZFbk4DW3wGP3pUz1UEusTPWUqRaUW5m+Vqilb2WqRSpXPWWqBeVWpq8VaomVqZ6GqqWwv/ECAAAAgEbBrxoCAAAAQGaFDF5mdpiZPW5mT5rZmUXUsFUti8zsITO738zmFXD8i81shZk9vNVlk8zsFjNbWPl3YoG1nGNmz1fun/vN7D01qmW6md1uZgvM7BEz+0Tl8prfN4laCrlvUD/K1Osq9RTW7+h1YS30OtQ9et1fHJte13ctpel1/dST9f6p+a8amlmTpCckHSJpiaR7JB3v7o/WtJA/17NI0ix3L+QcAmZ2oKR1ki5z970rl31d0ip3P7fSwCa6+2cKquUcSevc/Zu5j/+qWqZKmuru881snKR7JR0l6YOq8X2TqOVYFXDfoD6UrddValqkgvodvS6shV6Hukav+x/Hptf1XUtpel0/9WTtd0W84jVb0pPu/rS7d0n6saQjC6ijFNz9DkmrXnXxkZIurbx9qbZ8IRRVSyHcfZm7z6+8vVbSAknTVMB9k6gFSKHXbYVe1zd6HYYBet1W6HV9K1Ov66eerIoYvKZJWrzV+0tUbGN3STeb2b1mNqfAOrY2xd2XSVu+MCRNLrieU83swcpL1jV5CXhrZjZD0r6S7lbB982rapEKvm9QamXrdVL5+h29biv0OtQpel3/6HVbKVOv66MeKeP9U8TgZX1cVuRqxf3dfT9Jh0s6pfKyLP7sQkm7S9pH0jJJ59Xy4GY2VtJPJX3S3V+u5bEHUEuh9w1Kr2y9TqLfpdDr4lrodUih19UXel26nqz3TxGD1xJJ07d6fydJSwuoQ5Lk7ksr/66QdJ22vGRetOWV3z390++griiqEHdf7u497t4r6Yeq4f1jZi3a8mC4wt1/Vrm4kPumr1qKvG9QF0rV66RS9jt6neh1qHv0uv7R61SuXhfVk/v+KWLwukfSTDPb1cxaJR0n6cYC6pCZjan8QZ3MbIykQyU9nL5WTdwo6YTK2ydIuqGoQv70YKg4WjW6f8zMJF0kaYG7n79VVPP7JqqlqPsGdaM0vU4qbb+j19HrUP/odf2j15Wo16XqyX3/FHICZduymvHbkpokXezuX6l5EVvq2E1bfhIiSc2Srqx1LWZ2laSDJLVLWi7pbEnXS7pG0s6SnpN0jLtn/+PIoJaDtOXlVpe0SNJJf/pd3My1HCDpd5IektRbufgsbfn925reN4lajlcB9w3qR1l6XaWWQvsdvS6shV6Hukev+4vj0+v6rqU0va6ferL2u0IGLwAAAABoJIWcQBkAAAAAGgmDFwAAAABkxuAFAAAAAJkxeAEAAABAZgxeAAAAAJAZgxcAAAAAZMbgBQAAAACZMXgBAAAAQGYMXgAAAACQGYMXAAAAAGTG4AUAAAAAmTF4AQAAAEBmDF4AAAAAkBmDFwAAAABkxuAFAAAAAJkxeAEAAABAZgxeAAAAAJAZgxcAAAAAZMbgBQAAAACZMXgBAAAAQGYMXgAAAACQGYMXAAAAAGTG4AUAAAAAmTUP5spmdpik70hqkvQjdz839fHt7e2+yy4zBnNIYNiZP//ele7eUXQdiNHrgMGj15UfvQ4YvFSvq3rwMrMmSd+TdIikJZLuMbMb3f3R6Dq77DJDd909r9pDAsPSqBZ7tugaEKPXAUODXldu9DpgaKR63WB+1XC2pCfd/Wl375L0Y0lHDuL2AKCM6HUAGgG9DshsMIPXNEmLt3p/SeUyABhO6HUAGgG9DshsMIOX9XGZ/48PMptjZvPMbF7nys5BHA4ACkGvA9AI6HVAZoMZvJZImr7V+ztJWvrqD3L3ue4+y91ndbTzN7UA6g69DkAjoNcBmQ1m8LpH0kwz29XMWiUdJ+nGoSkLAEqDXgegEdDrgMyq3mro7t1mdqqkX2nL2tGL3f2RIasMAEqAXgegEdDrgPwGdR4vd79J0k1DVAsAlBK9DkAjoNcBeQ3mVw0BAAAAAAPA4AUAAAAAmTF4AQAAAEBmDF4AAAAAkBmDFwAAAABkNqithgAA1Kvunt4wa27i55IAgKHFdxYAAAAAyIzBCwAAAAAyY/ACAAAAgMwYvAAAAAAgMwYvAAAAAMiMwQsAAAAAMmOdPACg9Hp7Pcxee/rPw+zcD+0XZn+3146DqgkAgG3BK14AAAAAkBmDFwAAAABkxuAFAAAAAJkxeAEAAABAZgxeAAAAAJAZgxcAAAAAZMY6eQwZ93jds5nVsBIARUr1ghfWbAqzqx98Psy++NkfxAdM9JcTH7wvzE4747gwO/3A3cJsbFv8rbNpBL0OANA3XvECAAAAgMwYvAAAAAAgMwYvAAAAAMiMwQsAAAAAMmPwAgAAAIDMGLwAAAAAIDPWyWObrN/UHWZ3Pb0yzA7ec0qYpbYvs4YeKKee3nhl/MaunjDrTVxvvx3GxwccMzHO1iyPsxFNYXTB2d+PszHbhdmTN34uzCaMir+tNjfxs04Aad09vWHW1R1nqf5S7TOplmZ61lAb1OBlZoskrZXUI6nb3WcNRVEAUCb0OgCNgn4H5DMUr3i9093jlzoAYHig1wFoFPQ7IANeQwQAAACAzAY7eLmkm83sXjOb09cHmNkcM5tnZvM6V3YO8nAAUAh6HYBGkex39DqgeoMdvPZ39/0kHS7pFDM78NUf4O5z3X2Wu8/qaO8Y5OEAoBD0OgCNItnv6HVA9QY1eLn70sq/KyRdJ2n2UBQFAGVCrwPQKOh3QD5VL9cwszGSRrj72srbh0r60pBVhqw2J1aSTn5XvCpZmzeF0X7HHhVmB+4e/1RsZAt/aojyotf1rSlxHogxbfG3llQ2bdKoMLvnso+H2anXPhBm//Ku3cNsTdfmMHvT1Hh9fUtT/LlzCgzUM/rdtlm3MT7FzqbN8Wk1XnNE4i59ZU2cVdlfxu379jC7+ezDw2yPHcaG2YjUuYAQGsxWwymSrqt8k2mWdKW7/3JIqgKA8qDXAWgU9Dsgo6oHL3d/WtIbh7AWACgdeh2ARkG/A/Lid7wAAAAAIDMGLwAAAADIjMELAAAAADJj8AIAAACAzAaz1RDbyN0TWXy9HCs7W5rjmbvz9q+E2b6f/1WYzb/pjjCb+tuHwmzZNSeHWVtrU5gBaBw7t48Os/8+5W1V3Sar3wGkTq8jSbc8vjzM/umMK+Irdi6qsqIqjZ8cRmsf+H2Y/fWHnwmz73/t+DB79+5Twqxj/Mgwa3S84gUAAAAAmTF4AQAAAEBmDF4AAAAAkBmDFwAAAABkxuAFAAAAAJkxeAEAAABAZgxeAAAAAJAZ5/Gqoc6XN4VZrc950NsbnzjsqeXrw+yeLx0aZu84tzXMnn5sSZiNbInn/55EnU0Zzm8GIK9U7+lKnE/n8AvuDLPbT3/HoGoCUP9S50rdtDnuLbctXJG83VufWh2HOc7V1RQ/NZ/+rsPCbMqUsWE278Zb4+O9HH/+c2+Lz/H134+sDLNPv2P3MJu5Q1xnS1P1rwflOOdtDrziBQAAAACZMXgBAAAAQGYMXgAAAACQGYMXAAAAAGTG4AUAAAAAmTF4AQAAAEBmrJMfYus2dodZamW82dCvwUytVo2T9KrPlOtOfVuY7f2xq8Ls2ZWvhNnO24+uqhYA5TR/0UthtuPEtjC74Z/j/lKt1Gr7ellNDGCLlWu7wuy+5+OV8P/37vh0N5L06/+4vOqaQs3x6XdO+cKcMPvQfjuF2ZUPLg2zeb+Pr6fFj4bR/VdfG2fbTQ2z7//Dp8NsUWf8nG/3KWPCTJI298Q9e1RrU/K6ZcErXgAAAACQGYMXAAAAAGTG4AUAAAAAmTF4AQAAAEBmDF4AAAAAkBmDFwAAAABk1u86eTO7WNIRkla4+96VyyZJulrSDEmLJB3r7vGuzgYytq22G/pT65BXrotXq04YFdc5siVeybnmlc1h1twUz/Fnn/KOMEt5pasnzPpbHdrEOmhsA3rd0En1pbaWuE/M/vQNYfbcf/zjoGraVq9sik8NMnpk/ZyJJXVakRynMUF9GI79rrkp/no+4eu3htk+s3ZN37D3VltS6O0fPDbMzj5kjzDrSTyej/urHcPs/OXPxMW0joqz8ZPDaOc3vTHMUn0+9f9pRD89qau7+ueEZTGQV7wukXTYqy47U9Kt7j5T0q2V9wGgnl0ieh2AxnCJ6HdAzfU7eLn7HZJWveriIyVdWnn7UklHDW1ZAFBb9DoAjYJ+BxSj2r/xmuLuyySp8m/4WqSZzTGzeWY2r3NlZ5WHA4BC0OsANIoB9Tt6HVC97Ms13H2uu89y91kd7R25DwcAhaDXAWgE9DqgetUOXsvNbKokVf5dMXQlAUBp0OsANAr6HZBZtYPXjZJOqLx9gqR4FRUA1C96HYBGQb8DMhvIOvmrJB0kqd3Mlkg6W9K5kq4xsxMlPSfpmJxFNrqnlq8Ls+/+/tkwu2fB8jC767PvrKqW1IrQkc1x9q5d2sPsI1fMD7OH748/v2d/eHyYSVLTiPpYLYpyoNcNnTUb4tNOvP20y8PsgR99KMx6EivqRyROHZFap566XlvitBplk/ocE3ebEludMczVa79LnarihkeXhtnItpFh9sdrf5k+aOLxpbaxcZZYjX7OoXuGWXfic0ytTB+fOE2QLPE6yytr4mzTK2F0x+cODrOu7ngF/26Tx4TZyrXxaZCk+lkZn9Lv4OXu0bPb+B4HgDpDrwPQKOh3QDGyL9cAAAAAgEbH4AUAAAAAmTF4AQAAAEBmDF4AAAAAkBmDFwAAAABk1u9WQ5TbZd+5KszG7vFXYbahqyfMRo+MvyxGVrliecdJo8Ls3tvuC7PxO04Ns9TKVUmKl8cCGKzUWufU6vdFV84JswmjWwZVU18eW7o2zF58JV5dPHNyvCZ6yoS2QdU01Cyxtjq1Mj71/zC1ah8oSurr8pg37BRm1+8bn17ntwsXJI95xrkfD7NJiRXu799v5zBrS6xFb0p8jhs3x8/dDv36b8JM4zvirDe+zaZpM8Ns9fq4f+6UeM73SuL5Z+qURVL6vkmdViPVI2uNV7wAAAAAIDMGLwAAAADIjMELAAAAADJj8AIAAACAzBi8AAAAACAzBi8AAAAAyIx18nXggeUvxWF3vM5z3Zp1YZZayZnD9mNbw+zpq04Os4vueS7MyrMcFGg8qVXCL63fHGbjEyvjU+uAE5G2P/aHcfj8Y3HmvWE07o1vC7Onv/8PYdbcVK6fZ6bu066e+PNvG1HdqUOAorQkHntnvGv3MPv8u09N3u4vFq4Is0NfMyXMRo+MH0Op9ebJ0zwkrrf4qaVhtu873hhml33wI2G2XaJfp1a/P7l8fZhd++iyMHvguTVhJkmfOnC3MJu926QwS51Wo9bK9R0CAAAAAIYhBi8AAAAAyIzBCwAAAAAyY/ACAAAAgMwYvAAAAAAgMwYvAAAAAMiMdfIl0Z1Y6zt5VFuYfeD0E8LssvMuDbMDz/1NmO332o4w++Lf7BlmqZXxqfX1E8fE1zvlbbuG2chmfm4AFGVS4vE+IbGCeNW6eA19csVyap98d7y+Xr09VWVrH50fZrv9c3yTT1zwvjBra639ivbUfdrWwsp4DB+tiecEb91t+zB7anl86h1JOuOgeBV9c+K5TU9iLXxzYr/5iMRt3r9odZj98VvHhNmOE0eF2ehEX0q0j2Rv2XPq2DD797P+X5i1joy/r0jShx5dHmYP/5/3JK5Znn3yPHMFAAAAgMwYvAAAAAAgMwYvAAAAAMiMwQsAAAAAMmPwAgAAAIDMGLwAAAAAILN+18mb2cWSjpC0wt33rlx2jqSPSuqsfNhZ7n5TriKHi7Ub4pXHi1/cEGbfvP2pMNttyrj4gG1x9sTPr4+zG+MVqD/+RryS8+4bvhpme0xN1JkwKrHm1FPrpYFtRK/bNqlVwsnTY0yIT4+R8vNHlobZX73t9WH21OOTwuyVl+M10oe+Z58wu/nCy8Ls+dXxSuPdJo8Js2rvT0lqbuJnqNg2jdbvUqe02WlSvGpdkkaPjJ8qp56HpB7TqVXzG7ri01x87daFYXbR8fuGWVtLdT0i9TlUe70/fuPoMNv3o5ckb3dE05Qwu/APz4TZaQfEpwSotYH8n7hE0mF9XP4td9+n8t+weGACaGiXiF4HoDFcIvodUHP9Dl7ufoekVTWoBQAKQ68D0Cjod0AxBvP7Caea2YNmdrGZTRyyigCgXOh1ABoF/Q7IqNrB60JJu0vaR9IySedFH2hmc8xsnpnN61zZGX0YAJQRvQ5AoxhQv6PXAdWravBy9+Xu3uPuvZJ+KGl24mPnuvssd5/V0d5RbZ0AUHP0OgCNYqD9jl4HVK+qwcvMpm717tGSHh6acgCgPOh1ABoF/Q7IbyDr5K+SdJCkdjNbIulsSQeZ2T6SXNIiSSflK7G+bO6O1/7ufES8bl1rV4bRjVd8Icw+eMGd8W2uXx1n1a5iT1zvLR/6fpit/uVnqjteQrVrToG+0Ou2TW9iHXJzU/zY3LQ5XpW8bmN3mE1obQmzW/7l7WE2siU+JUVKqpefPCqu5W1nXBdmj1/4j2E2PnGbwFCj3/1Zal18f6p9HpJaQ78x0SMv+af9wqw5sTI/tU6/1s+lNm5OnB7D06fOOOTAmWH2i/teCLMyrZPv96vN3Y/v4+KLMtQCAIWh1wFoFPQ7oBicdREAAAAAMmPwAgAAAIDMGLwAAAAAIDMGLwAAAADIjMELAAAAADKrfodmg+pJrFCWpC//emEctrSF0Qc+e3KYXfvwijBrbkn8L+yNV5Jq6h5x1tIaZ0ufCKPXvv3N8fUADCu9iXXI1z30fJgtXdsVZh+dvUuYvXmXSWHW0jT0P0NsaY5v87tH7R1mXzv8tWGWWtu8KbG+fkNXopdLmjQ20bMBFCK1Mv6p5evD7LRrHwizqz4UP88a1xb3gTKdfqclcbqRaa97TfK6v7ztsTB78nv/UHVNtcQrXgAAAACQGYMXAAAAAGTG4AUAAAAAmTF4AQAAAEBmDF4AAAAAkBmDFwAAAABkxjr5bTSin42cn3/3zDD7wiFnhFlzleuQFx0wI8xOubY9zJ55ZnWYTZkyJsyeeiJe6XzWkfEaZQDDyyHf/l2YvfDCujh77oUwO+2qD4dZat36iP4a8xAb0xZ/60xlvYnTkdz99Kowe+3Uccl6Umury7RGGhhuUo+91OmHdpwYn17oF//8tjBLrWKv9WM99fm9vGFzmM36+NVh9o6D41N1SNKPPveuMGtNnAKkTOqjSgAAAACoYwxeAAAAAJAZgxcAAAAAZMbgBQAAAACZMXgBAAAAQGYMXgAAAACQGevkt1F/6zpbm2u7znNGR7z6/VtHxWs5d5o0KsxeWLMpzD553UNhdsTrp4YZgOHl/t/cF2ZjpuwQZquvPrGq441qbarqerW2al1XmI0fFX/LPeWye8PsljPekTwmK+OBfFKngfjpg0vC7ObH4lNEzJ4xPsyOfF38XGryhHgNfQ6pz31h4rQh9yyNP3c990gYTZ345mQ9241uCbN66YO84gUAAAAAmTF4AQAAAEBmDF4AAAAAkBmDFwAAAABkxuAFAAAAAJkxeAEAAABAZqyTr3OpVZ+7TY5XzTc3xTN3x7j4Nm886a0DKwzAsPb4FR8Ls3Ft8beWVM8aMWLo1wG7x8fr7omzzT29YTayJV5tn1p7v35TT5jdnFgZv/24kWEGYHC6E491SUq0LI1vjdebP/BEZ5j95o+LwuzYr+6UrKeWurrj++bffv1EmP3X5b8Ksz/+9Ith9uFL5yXrST13rRf9fgZmNt3MbjezBWb2iJl9onL5JDO7xcwWVv6dmL9cAMiDXgegEdDrgOIMZHTslnS6u79O0lslnWJme0k6U9Kt7j5T0q2V9wGgXtHrADQCeh1QkH4HL3df5u7zK2+vlbRA0jRJR0q6tPJhl0o6KlONAJAdvQ5AI6DXAcXZpl+WNLMZkvaVdLekKe6+TNryIJY0ObjOHDObZ2bzOlfGv+8KAGVBrwPQCOh1QG0NePAys7GSfirpk+7+8kCv5+5z3X2Wu8/qaO+opkYAqBl6HYBGQK8Dam9Ag5eZtWjLg/MKd/9Z5eLlZja1kk+VtCJPiQBQG/Q6AI2AXgcUo9918mZmki6StMDdz98qulHSCZLOrfx7Q5YKkWSJ7csLl60Ls+3HtobZ5AltgykJqEv0um3TMS7uIZZqTBn0JPY9H3bBXWF20kEzwuymR1eG2dxj3xBmLU3x5948Il4139Icr6UGhhK97i/1t6J83tOrw+z9X7s5zMaMj0/p89Izz4RZa3NtV6ZvTqyMP+RbvwuzRU8tD7O3/H18eowv3hKvob/rs+8Ms+FiIOfx2l/S+yU9ZGb3Vy47S1semNeY2YmSnpN0TJYKAaA26HUAGgG9DihIv4OXu98pKfoR3sFDWw4AFINeB6AR0OuA4tT/KaABAAAAoOQYvAAAAAAgMwYvAAAAAMiMwQsAAAAAMmPwAgAAAIDMBrJOHkPEPT7XTMqq9ZvD7H//8vEw+8xBu4dZ+7iRVdWSQ3dPfA6J/s6vAQDPr9oQZnOP3zfMDjv3tjC7/X+/O8xSfanaPg+gnPadsV2YXfyZuE+8/7QfxDe6blUYrVy7Kcymbz86vs2EjZt7wuwjV90fZk8+9nyY7bhzR5hd+5G3hNnYtsYePXhWCwAAAACZMXgBAAAAQGYMXgAAAACQGYMXAAAAAGTG4AUAAAAAmTF4AQAAAEBmjb3TMYPUanQzC7MRcaTtRreE2b+/d+/E8eLbzGFjV7yudGRLPONv2sw6eWA42dyd6oPx9ap9vO/cXt2K5cfP/7uqrpeS6vMA6k9T4gnaEXvvGGZP/fzzYfapGx4Js2/duSjMbrztiTB78f/dEWbq7oqz0RPC6PLvnxpmf/v6qfFtIsSzWgAAAADIjMELAAAAADJj8AIAAACAzBi8AAAAACAzBi8AAAAAyIzBCwAAAAAya9h18u6eyOLr9aZCpdeOVrtmuKlOthO3tTaFWer+HtPWsF+GQN1K9bOW5jppWgCQyajEc6Ib/vPm+IovLh76Yppbw+hTnz8hzN6z1w5DX0uD4xUvAAAAAMiMwQsAAAAAMmPwAgAAAIDMGLwAAAAAIDMGLwAAAADIjMELAAAAADLrd4+3mU2XdJmkHST1Sprr7t8xs3MkfVRSZ+VDz3L3m3IVOtRSq5BTW99HiDXJ1ah2lT5QK8O11wHA1uh1tZFaJ//Cz/81zDrXdoXZ/UtXh9m7Zk4Os9EjOW1PWQzk/0S3pNPdfb6ZjZN0r5ndUsm+5e7fzFceANQMvQ5AI6DXAQXpd/By92WSllXeXmtmCyRNy10YANQSvQ5AI6DXAcXZpr/xMrMZkvaVdHflolPN7EEzu9jMJgbXmWNm88xsXufKzr4+BABKhV4HoBHQ64DaGvDgZWZjJf1U0ifd/WVJF0raXdI+2vKTk/P6up67z3X3We4+q6O9Y/AVA0BG9DoAjYBeB9TegAYvM2vRlgfnFe7+M0ly9+Xu3uPuvZJ+KGl2vjIBID96HYBGQK8DitHv4GVb1tFdJGmBu5+/1eVTt/qwoyU9PPTlAUBt0OsANAJ6HVCcgWw13F/S+yU9ZGb3Vy47S9LxZraPJJe0SNJJGeoDgFqh1wFoBPS6go1siVfN7zRpVFUZ6sNAthreKfV58irO7QBg2KDXAWgE9DqgONu01RAAAAAAsO0YvAAAAAAgMwYvAAAAAMiMwQsAAAAAMmPwAgAAAIDMGLwAAAAAIDMGLwAAAADIjMELAAAAADJj8AIAAACAzBi8AAAAACAzBi8AAAAAyIzBCwAAAAAyM3ev3cHMOiU9W3m3XdLKmh28f2Wqh1piZapnqGrZxd07huB2UBKv6nXS8Py6HQrUEitTPfQ69IleN2BlqkUqVz3DsZaw19V08PqLA5vNc/dZhRy8D2Wqh1piZaqnTLWg3Mr0tUItfStTLVK56ilTLSi3Mn2tUEusTPU0Wi38qiEAAAAAZMbgBQAAAACZFTl4zS3w2H0pUz3UEitTPWWqBeVWpq8VaulbmWqRylVPmWpBuZXpa4VaYmWqp6FqKexvvAAAAACgUfCrhgAAAACQGYMXAAAAAGRWyOBlZoeZ2eNm9qSZnVlEDVvVssjMHjKz+81sXgHHv9jMVpjZw1tdNsnMbjGzhZV/JxZYyzlm9nzl/rnfzN5To1qmm9ntZrbAzB4xs09ULq/5fZOopZD7BvWjTL2uUk9h/Y5eF9ZCr0Pdo9f9xbHpdX3XUppe1089We+fmv+Nl5k1SXpC0iGSlki6R9Lx7v5oTQv5cz2LJM1y90JO3mZmB0paJ+kyd9+7ctnXJa1y93MrDWyiu3+moFrOkbTO3b+Z+/ivqmWqpKnuPt/Mxkm6V9JRkj6oGt83iVqOVQH3DepD2XpdpaZFKqjf0evCWuh1qGv0uv9xbHpd37WUptf1U0/WflfEK16zJT3p7k+7e5ekH0s6soA6SsHd75C06lUXHynp0srbl2rLF0JRtRTC3Ze5+/zK22slLZA0TQXcN4lagBR63VbodX2j12EYoNdthV7XtzL1un7qyaqIwWuapMVbvb9ExTZ2l3Szmd1rZnMKrGNrU9x9mbTlC0PS5ILrOdXMHqy8ZF2Tl4C3ZmYzJO0r6W4VfN+8qhap4PsGpVa2XieVr9/R67ZCr0Odotf1j163lTL1uj7qkTLeP0UMXtbHZUXutN/f3feTdLikUyovy+LPLpS0u6R9JC2TdF4tD25mYyX9VNIn3f3lWh57ALUUet+g9MrW6yT6XQq9Lq6FXocUel19odel68l6/xQxeC2RNH2r93eStLSAOiRJ7r608u8KSddpy0vmRVte+d3TP/0O6oqiCnH35e7e4+69kn6oGt4/ZtaiLQ+GK9z9Z5WLC7lv+qqlyPsGdaFUvU4qZb+j14leh7pHr+sfvU7l6nVRPbnvnyIGr3skzTSzXc2sVdJxkm4soA6Z2ZjKH9TJzMZIOlTSw+lr1cSNkk6ovH2CpBuKKuRPD4aKo1Wj+8fMTNJFkha4+/lbRTW/b6JairpvUDdK0+uk0vY7eh29DvWPXtc/el2Jel2qntz3T823GkqSbVnN+G1JTZIudvev1LyILXXspi0/CZGkZklX1roWM7tK0kGS2iUtl3S2pOslXSNpZ0nPSTrG3bP/cWRQy0Ha8nKrS1ok6aQ//S5u5loOkPQ7SQ9J6q1cfJa2/P5tTe+bRC3Hq4D7BvWjLL2uUkuh/Y5eF9ZCr0Pdo9f9xfHpdX3XUppe1089WftdIYMXAAAAADSSQk6gDAAAAACNhMELAAAAADJj8AIAAACAzBi8AAAAACAzBi8AAAAAyIzBCwAAAAAyY/ACAAAAgMz+f3eyLRd9sNyeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time \n",
    "\n",
    "D = Discriminator()\n",
    "G = Generator()\n",
    "epochs = 8\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    print (\"epoch = \", epoch + 1)\n",
    "    for j in range(5000):\n",
    "        D.train(train[random.choices(range(train.shape[0]),k=100)], torch.ones(100,1)) # 直接choices会变成一个列表\n",
    "        D.train(G.model(torch.randn(100,100)).detach(), torch.zeros(100,1))\n",
    "        G.train(D, torch.randn(100,100))\n",
    "    show(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df58f930-1796-4eb6-8d1d-d428c6a40bb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time \n",
    "\n",
    "D = Discriminator()\n",
    "G = Generator()\n",
    "epochs = 8\n",
    "\n",
    "for epoch in range(epochs):\n",
    "    print (\"epoch = \", epoch + 1)\n",
    "    for j in range(5000):\n",
    "        D.train(train[random.choices(range(train.shape[0]),k=1)], torch.ones(1,1)) # 直接choices会变成一个列表\n",
    "        D.train(G.model(torch.randn(100,100)).detach(), torch.zeros(100,1))\n",
    "        G.train(D, torch.randn(100,100))\n",
    "    show(G)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
